样式: 排序: IF: - GO 导出 标记为已读
-
Public cloud object storage auditing: Design, implementation, and analysis J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-03-09 Fei Chen, Fengming Meng, Zhipeng Li, Li Li, Tao Xiang
Cloud storage auditing is a technique that enables a user to remotely check the integrity of the outsourced data in the cloud storage. Although researchers have proposed various protocols for cloud storage auditing, the proposed schemes are theoretical in nature, which are not fit for existing mainstream cloud storage service practices. To bridge this gap, this paper proposes a cloud storage auditing
-
Dataflow optimization with layer-wise design variables estimation method for enflame CNN accelerators J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-03-06 Tian Chen, Yu-an Tan, Zheng Zhang, Nan Luo, Bin Li, Yuanzhang Li
As convolution layers have been proved to be the most time-consuming operation in convolutional neural network (CNN) algorithms, many efficient CNN accelerators have been designed to boost the performance of convolution operations. Previous works on CNN acceleration usually use fixed design variables for diverse convolutional layers, which would lead to inefficient data movements and low utilization
-
Adaptive patch grid strategy for parallel protein folding using atomic burials with NAMD J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-03-04 Emerson A. Macedo, Alba C.M.A. Melo
The definition of protein structures is an important research topic in molecular biology currently, since there is a direct relationship between the function of the protein in the organism and the 3D geometric configuration it adopts. The transformations that occur in the protein structure from the 1D configuration to the 3D form are called protein folding. protein folding methods use physical forces
-
HoneyTwin: Securing smart cities with machine learning-enabled SDN edge and cloud-based honeypots J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-02-20 Mohammed M. Alani
With the promise of higher throughput, and better response times, 6G networks provide a significant enabler for smart cities to evolve. The rapidly-growing reliance on connected devices within the smart city context encourages malicious actors to target these devices to achieve various malicious goals. In this paper, we present a novel defense technique that creates a cloud-based virtualized honeypot/twin
-
Hierarchical sort-based parallel algorithm for dynamic interest matching J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-02-18 Wenjie Tang, Yiping Yao, Lizhen Ou, Kai Chen
Publish–subscribe communication is a fundamental service used for message-passing between decoupled applications in distributed simulation. When abundant unnecessary data transfer is introduced, interest-matching services are needed to filter irrelevant message traffic. Frequent demands during simulation execution makes interest matching a bottleneck with increased simulation scale. Contemporary algorithms
-
Revisiting I/O bandwidth-sharing strategies for HPC applications J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-02-15 Anne Benoit, Thomas Herault, Lucas Perotin, Yves Robert, Frédéric Vivien
This work revisits I/O bandwidth-sharing strategies for HPC applications. When several applications post concurrent I/O operations, well-known approaches include serializing these operations (▪) or fair-sharing the bandwidth across them (). Another recent approach, I/O-Sets, assigns priorities to the applications, which are classified into different sets based upon the average length of their iterations
-
-
Front Matter 1 - Full Title Page (regular issues)/Special Issue Title page (special issues) J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-02-12
-
Exploring multiprocessor approaches to time series analysis J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-02-08 Ricardo Quislant, Eladio Gutierrez, Oscar Plata
Time series analysis is a key technique for extracting and predicting events in domains as diverse as epidemiology, genomics, neuroscience, environmental sciences, economics, etc. , a state-of-the-art algorithm to perform time series analysis, finds out the most similar and dissimilar subsequences in a time series in deterministic time and it is exact. Matrix Profile has low arithmetic intensity and
-
Fast recovery for large disk enclosures based on RAID2.0: Algorithms and evaluation J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-02-07 Qiliang Li, Min Lyu, Liangliang Xu, Yinlong Xu
The RAID2.0 architecture, which uses dozens or even hundreds of disks, is widely adopted for large-capacity data storage. However, limited resources like memory and CPU cause RAID2.0 to execute batch recovery for disk failures. The traditional random data placement and recovery schemes result in highly skewed I/O access within a batch, which slows down the recovery speed. To address this issue, we
-
Evaluating the effectiveness of Bat optimization in an adaptive and energy-efficient network-on-chip routing framework J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-02-05 B. Naresh Kumar Reddy, Aruru Sai Kumar
Adaptive routing is effective in maintaining higher processor performance and avoids packets over minimal or non-minimal alternate routes without congestion for a multiprocessor system on chip. However, many systems cannot deal with the fact that sending packets over an alternative path rather than the shorter, fixed-priority route can result in packets arriving at the destination node out of order
-
Collaborative dispersion by silent robots J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-02-05 Barun Gorain, Partha Sarathi Mandal, Kaushik Mondal, Supantha Pandit
In the dispersion problem, a set of co-located mobile robots must relocate themselves in distinct nodes of an unknown network. The network is modeled as an anonymous graph , where the graph's nodes are not labeled. The edges incident to a node with degree are labeled with port numbers in the range at . The robots have unique IDs in the range , where , and are initially placed at a source node . The
-
Energy-efficient offloading for DNN-based applications in edge-cloud computing: A hybrid chaotic evolutionary approach J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-02-01 Zengpeng Li, Huiqun Yu, Guisheng Fan, Jiayin Zhang, Jin Xu
The rapid development of Deep Neural Networks (DNNs) lays solid foundations for Internet of Things systems. However, mobile devices with limited processing capacity and short battery life confront the difficulties of executing complex DNNs. To satisfy different Quality of Service requirements, a feasible solution is offloading DNN layers to edge nodes and the cloud. The energy-efficient offloading
-
DQS: A QoS-driven routing optimization approach in SDN using deep reinforcement learning J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-02-01 Lizeth Patricia Aguirre Sanchez, Yao Shen, Minyi Guo
In recent decades, the exponential growth of applications has intensified traffic demands, posing challenges in ensuring optimal user experiences within modern networks. Traditional congestion avoidance and control mechanisms embedded in conventional routing struggle to promptly adapt to new-generation networks. Current routing approaches risk-averse outcomes such as (1) scalability constraints, (2)
-
An edge architecture for enabling autonomous aerial navigation with embedded collision avoidance through remote nonlinear model predictive control J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-01-29 Achilleas Santi Seisa, Björn Lindqvist, Sumeet Gajanan Satpute, George Nikolakopoulos
-
A multi-objective grey-wolf optimization based approach for scheduling on cloud platforms J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-01-22 Minhaj Ahmad Khan, Raihan ur Rasool
A cloud computing environment processes user workloads or tasks by exploiting its high performance computational, storage, of reducing and network resources. The virtual machines in the cloud environment are allocated to tasks with the aim of reducing overall execution time. The use of high performance resources incurs monetary costs, as well as high power consumption. The heuristic based approaches
-
An active queue management for wireless sensor networks with priority scheduling strategy J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-01-26 Changzhen Zhang, Jun Yang, Ning Wang
In Wireless Sensor Networks (WSNs), the packet congestion will lead to high delay and high packet loss rate, which severely affects the timely transmission of real-time packets. As a congestion control method, Random Early Detection (RED) is able to stabilize the queue length at a low level. However, it does not classify the data of WSNs to achieve a targeted queue management. Since real-time packets
-
Antipaxos: Taking interactive consistency to the next level J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-01-17 Chunyu Mao, Wojciech Golab, Bernard Wong
Classical Paxos-like consensus protocols limit system scalability due to a single leader and the inability to process conflicting proposals in parallel. We introduce a novel agreement protocol, called Antipaxos, that instead reaches agreement on a collection of proposals using an efficient leaderless fast path when the environment is synchronous and failure-free, and falls back on a more elaborate
-
Front Matter 1 - Full Title Page (regular issues)/Special Issue Title page (special issues) J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-01-16
-
-
HyLAC: Hybrid linear assignment solver in CUDA J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-01-15 Samiran Kawtikwar, Rakesh Nagi
The Linear Assignment Problem (LAP) is a fundamental combinatorial optimization problem with a wide range of applications. Over the years, significant progress has been made in developing efficient algorithms to solve the LAP, particularly in the realm of high-performance computing, leading to remarkable reductions in computation time. In recent years, hardware improvements in General Purpose Graphics
-
Reliable IoT analytics at scale J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-01-15 Panagiotis Gkikopoulos, Peter Kropf, Valerio Schiavoni, Josef Spillner
Societies and legislations are moving towards automated decision-making based on measured data in safety-critical environments. Over the next years, density and frequency of measurements will increase to generate more insights and get a more solid basis for decisions, including through redundant low-cost sensor deployments. The resulting data characteristics lead to large-scale system design in which
-
Speedup and efficiency of computational parallelization: A unifying approach and asymptotic analysis J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-01-10 Guido Schryen
In high performance computing environments, we observe an ongoing increase in the available number of cores. For example, the current TOP500 list reveals that nine clusters have more than 1 million cores. This development calls for re-emphasizing performance (scalability) analysis and speedup laws as suggested in the literature (e.g., Amdahl's law and Gustafson's law), with a focus on asymptotic performance
-
Proactive auto-scaling technique for web applications in container-based edge computing using federated learning model J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-01-09 Javad Dogani, Farshad Khunjush
Edge computing has emerged as an attractive alternative to traditional cloud computing by utilizing processing, network, and storage resources close to end devices, such as Internet of Things (IoT) sensors. Edge computing is still in its infancy, and resource provisioning and service scheduling remain research concerns. Kubernetes is a container orchestration tool in distributed environments. Proactive
-
An efficient sequential consistency implementation with dynamic race detection for GPUs J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-01-06 Abdulaziz Tabbakh, Murali Annavaram
As GPUs are being used for general purpose computations, applications with different memory access requirements have emerged. In spite of the growing demand, only few GPU coherence protocols and memory models have been explored in research, and even fewer models have been implemented in products. However, in the CPU domain a diverse range of memory models for parallel programming have been proposed
-
Modified smell detection algorithm for optimal paths engineering in hybrid SDN J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-01-04 S.S. V̄inod Chandra, S. Anand Hareendran
Optimal path engineering in hybrid software defined networking environments involves leveraging traditional networking and software defined capabilities to determine the most efficient paths for network traffic. In hybrid software defined networks, some parts of the network may be controlled by controllers, while others rely on traditional routing protocols. Leveraging the OpenFlow protocol, widely
-
Data-centric workloads with MPI_Sort J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2024-01-02 P. Zulian, S. Ben Bader, G. Fourestey, R. Krause, D. Rossinelli
Sorting is a fundamental task in computing and plays a central role in information technology. The advent of rack-scale and warehouse-size data processing shaped the architecture of data analysis platforms towards supercomputing. In turn, established techniques on supercomputers have become relevant to a wider range of application domains. This work is concerned with multi-way mergesort with exact
-
Evaluating performance portability of five shared-memory programming models using a high-order unstructured CFD solver J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-12-29 Zhe Dai, Liang Deng, YongGang Che, Ming Li, Jian Zhang, Yueqing Wang
This paper presents implementing and optimizing a high-order unstructured computational fluid dynamics (CFD) solver using five shared-memory programming models: CUDA, OpenACC, OpenMP, Kokkos, and OP2. The study aims to evaluate the performance of these models on different hardware architectures, including NVIDIA GPUs, x86-based Intel/AMD, and Arm-based systems. The goal is to determine whether these
-
A unified hybrid memory system for scalable deep learning and big data applications J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-12-28 Wei Rang, Huanghuang Liang, Ye Wang, Xiaobo Zhou, Dazhao Cheng
Emerging non-volatile memory (NVM) technologies are of dynamic random access memory (DRAM)-like, high capacity, and low cost, at the expense of slower bandwidth and higher read/write latency compared to DRAM. Typically, NVM finds its primary application in serving as an extension of conventional DRAM to create hybrid memory systems tailored to non-uniform memory access (NUMA) architectures. This strategic
-
GPU-optimized approaches to molecular docking-based virtual screening in drug discovery: A comparative analysis J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-12-19 Emanuele Vitali, Federico Ficarelli, Mauro Bisson, Davide Gadioli, Gianmarco Accordi, Massimiliano Fatica, Andrea R. Beccari, Gianluca Palermo
Finding a novel drug is a very long and complex procedure. Using computer simulations, it is possible to accelerate the preliminary phases by performing a virtual screening that filters a large set of drug candidates to a manageable number. This paper presents the implementations and comparative analysis of two GPU-optimized implementations of a virtual screening algorithm targeting novel GPU architectures
-
An efficient SSSP algorithm on time-evolving graphs with prediction of computation results J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-12-19 Yongli Cheng, Chuanjie Huang, Hong Jiang, Xianghao Xu, Fang Wang
Many applications need to execute Single-Source Shortest Paths (SSSP) algorithm on each snapshot of a time-evolving graph, leading to long waiting times experienced by the users of such applications. However, these applications are often time-sensitive, the delayed computation results can lead to the loss of best decision-making opportunities. To address this problem, in this paper we propose an efficient
-
Read/write fence-free work-stealing with multiplicity J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-12-19 Armando Castañeda, Miguel Piña
It has been shown that any nonblocking algorithm for work-stealing in the standard asynchronous shared memory model of computation must use expensive Read-After-Write synchronization patterns or atomic Read-Modify-Write instructions. Algorithms for relaxations of work-stealing have been proposed, which only partially avoid this impossibility result. In restricted models of computation, work-stealing
-
Starlight: A kernel optimizer for GPU processing J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-12-22 Alberto Zeni, Emanuele Del Sozzo, Eleonora D'Arnese, Davide Conficconi, Marco D. Santambrogio
Over the past few years, GPUs have found widespread adoption in many scientific domains, offering notable performance and energy efficiency advantages compared to CPUs. However, optimizing GPU high-performance kernels poses challenges given the complexities of GPU architectures and programming models. Moreover, current GPU development tools provide few high-level suggestions and overlook the underlying
-
Characterization of matroidal connectivity of regular networks J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-12-16 Hong Zhang, Shuming Zhou
High performance computing system, which takes an interconnection network as its infrastructure topology, has been utilized in scientific computing, big data analysis as well as artificial intelligence. With the rapid growth of infrastructure topology (interconnection network) in high performance computing system, the probability of network element malfunction increases dramatically. Generally, the
-
Hotspot resolution in cloud computing: A Γ-robust knapsack approach for virtual machine migration J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-12-12 Jiaxi Wu, Wenquan Yang, Xinming Han, Yunzhe Qiu, Andrei Gudkov, Jie Song
Cloud providers offer virtual machines (VMs) located on physical machines (PMs) to meet the increasing demand for computational services. When the instantaneous utilized capacities of VMs exceed a PM's threshold, hotspots form and degrade VM performance. To resolve hotspots, some VMs must be live migrated to other PMs, but selecting which VMs is challenging as their utilized capacities change continuously
-
Stab-FD: A cooperative and adaptive failure detector for wide area networks J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-11-30 Pierre Sens, Luciana Arantes, Anubis Graciela De Moraes Rossetto, Olivier Marin
Failure detectors (FDs) are a fundamental abstraction that plays a central role in the design of distributed systems. FDs are distributed oracles that provide processes with unreliable information about process failures, often in the form of a list of trusted or suspected process identities. In this article, we propose a timer-based FD which assesses the quality of its input links, and exchanges its
-
Impact of federated deep learning on vehicle-based speed control in mixed traffic flows J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-11-27 Martin Gregurić, Filip Vrbanić, Edouard Ivanjko
This study investigates the application of Federated Learning (FL) in an environment of mixed traffic flow with Connected and Automated Vehicles (CAVs). The focus of this study is set on the CAV cruise control speed adjustment which is enforced by the Intelligent Speed Adaptation (ISA) system. It is modelled as an Actor-Critic-based learning architecture with the goal of computing the vehicle speed
-
Sketch-fusion: A gradient compression method with multi-layer fusion for communication-efficient distributed training J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-11-24 Lingfei Dai, Luqi Gong, Zhulin An, Yongjun Xu, Boyu Diao
Gradient compression is an effective technique for improving the efficiency of distributed training. However, introducing gradient compression can reduce model accuracy and training efficiency. Furthermore, we also find that using a layer-wise gradient compression algorithm would lead to significant compression and communication overhead, which can negatively impact the scaling efficiency of the distributed
-
Eventually lattice-linear algorithms J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-11-22 Arya Tanmay Gupta, Sandeep S. Kulkarni
Lattice-linear systems allow nodes to execute asynchronously. We introduce eventually lattice-linear algorithms, where lattices are induced only among the states in a subset of the state space. The algorithm guarantees that the system transitions to a state in one of the lattices. Then, the algorithm behaves lattice linearly while traversing to an optimal state through that lattice. We present a lattice-linear
-
Distributed runtime verification of metric temporal properties J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-11-22 Ritam Ganguly, Yingjie Xue, Aaron Jonckheere, Parker Ljung, Benjamin Schornstein, Borzoo Bonakdarpour, Maurice Herlihy
Distributed Systems are often composed of geographically separated components, where the clocks may not be perfectly synchronized. As such verifying the correctness of such system properties are a major challenge and are of utmost importance. In this paper, we describe a centralized runtime monitoring technique for distributed system. First, we propose a generalized runtime verification technique for
-
A survey of machine learning for Network-on-Chips J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-11-20 Xiaoyun Zhang, Dezun Dong, Cunlu Li, Shaocong Wang, Liquan Xiao
The popularity of Machine Learning (ML) has extended to numerous disciplines, including the domain of Network-on-chips (NoCs), leading to a consequential impact. Recent works have explored ML models' applicability for NoCs design, optimization, and performance evaluation. ML-based NoCs design has demonstrated superior performance to heuristic methods employed by human experts in NoCs design. This has
-
ML-driven risk estimation for memory failure in a data center environment with convolutional neural networks, self-supervised data labeling and distribution-based model drift determination J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-11-18 Tim Breitenbach, Shrikanth Malavalli Divakar, Lauritz Rasbach, Patrick Jahnke
With the trend towards multi-socket server systems, the demand for random access memory (RAM) per server increased. The consequence are more DIMM sockets per server. Since every dual in-line memory module (DIMM), which comprises a series of dynamic random-access memory integrated circuits, has a probability of failure, RAM issues became a dominant failure pattern for servers. The concept introduced
-
MSLShard: An efficient sharding-based trust management framework for blockchain-empowered IoT access control J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-11-07 Jin Tian, JunFeng Tian, RuiZhong Du
The Internet of Things (IoT) is widely used in modern smart areas such as smart cities, but data security remains a significant challenge. Blockchain-based IoT access control addresses data security issues by preventing unauthorized devices from accessing limited IoT resources. However, most existing blockchain-based access control schemes still have scalability and security issues. A lightweight and
-
Revisiting thread configuration of SpMV kernels on GPU: A machine learning based approach J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-11-10 Jianhua Gao, Weixing Ji, Jie Liu, Yizhuo Wang, Feng Shi
Sparse matrix-vector multiplication (SpMV) optimization on GPUs has been challenging due to irregular memory accesses and unbalanced workloads. The majority of existing solutions assign a fixed number of threads to one or more rows of sparse matrices according to empirical formulas. However, this method does not give the optimal thread configuration and results in a significant performance loss. This
-
MSHGN: Multi-scenario adaptive hierarchical spatial graph convolution network for GPU utilization prediction in heterogeneous GPU clusters J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-11-07 Sheng Wang, Shiping Chen, Fei Meng, Yumei Shi
Accurately predicting GPU utilization is crucial for effectively managing heterogeneous GPU clusters, yet existing prediction methods are tailored to homogeneous clusters or ignore the unique characteristics of heterogeneous ones. To address this problem, we propose the Multi-Scenarios Adaptive Hierarchical Spatial Graph Convolution Network (MSHGN) model. This model leverages the hierarchical relationships
-
Effectively computing high strength mixed covering arrays with constraints J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-11-08 Carlos Ansótegui, Eduard Torres
Covering arrays have become a key piece in Combinatorial Testing. In particular, we focus on the efficient construction of Covering Arrays with Constraints of high strength. SAT solving technology has been proven to be well suited when solving Covering Arrays with Constraints. However, the size of the SAT reformulations rapidly grows up with higher strengths. To this end, we present a new incomplete
-
Exploring energy saving opportunities in fault tolerant HPC systems J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-11-07 Marina Morán, Javier Balladini, Dolores Rexachs, Enzo Rucci
Nowadays, improving the energy efficiency of high-performance computing (HPC) systems is one of the main drivers in scientific and technological research. As large-scale HPC systems require some fault-tolerant method, the opportunities to reduce energy consumption should be explored. In particular, rollback-recovery methods using uncoordinated checkpoints prevent all processes from re-executing when
-
A parallel fractional explicit group modified AOR iterative method for solving fractional Poisson equation with multi-core architecture J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-10-31 Nik Amir Syafiq, Mohamed Othman, Norazak Senu, Fudziah Ismail, Nor Asilah Wati Abdul Hamid
This research studies the Multi-core Architecture for the Modified Accelerated Overrelaxation (MAOR) scheme for solving the fractional Poisson equation. The equation is discretized using the fractional explicit group (FEG) finite difference method. This research also presents the parallel implementation of the fractional order iterative methods with the chessboard (CB) ordering strategies. The experimental
-
Multi-resource scheduling of moldable workflows J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-10-24 Lucas Perotin, Sandhya Kandaswamy, Hongyang Sun, Padma Raghavan
Resource scheduling plays a vital role in High-Performance Computing (HPC) systems. Most scheduling research in HPC has focused on only a single type of resource (e.g., computing cores or I/O resource). With the advancement in hardware architectures and the increase in data-intensive HPC applications, there is a need to simultaneously consider a diverse set of resources (e.g., computing cores, cache
-
An evaluation of GPU filters for accelerating the 2D convex hull J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-10-27 Roberto Carrasco, Héctor Ferrada, Cristóbal A. Navarro, Nancy Hitschfeld
The Convex Hull is one of the most relevant structures in computational geometry, with many applications such as in computer graphics, robotics, and data mining. Despite the advances in the new algorithms in this area, it is often needed to improve the performance to solve more significant problems quickly or in real-time processing. This work presents an experimental evaluation of GPU filters to reduce
-
Scalable atomic broadcast: A leaderless hierarchical algorithm J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-10-23 Lucas V. Ruchel, Edson Tavares de Camargo, Luiz Antonio Rodrigues, Rogério C. Turchetti, Luciana Arantes, Elias Procópio Duarte
Atomic Broadcast is an essential broadcast primitive as it ensures the consistency of distributed replicas. However, it is notoriously non-scalable. In this work, we introduce the Leaderless Hierarchical Atomic Broadcast (LHABcast) algorithm, which has two properties to improve scalability. First, it is a fully decentralized algorithm that does not rely on a sequencer/leader, which is often a significant
-
GPU-accelerated transportation simplex algorithm J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-10-24 Mohit Mahajan, Rakesh Nagi
Transportation Problem (TP) is a popular linear program for optimally matching several supply centers to several demand centers at the smallest transportation cost. Recent disruptions in the physical supply chains and the growth of internet marketplaces such as ride-sharing, doorstep delivery, and expedited shipping have engendered a need for efficient algorithms to solve large-scale TPs in near real-time
-
Vampire: A smart energy meter for synchronous monitoring in a distributed computer system J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-10-24 Antonio F. Díaz, Beatriz Prieto, Juan José Escobar, Thomas Lampert
This paper presents the design and implementation of a low-cost system oriented to the synchronised and real-time surveillance and monitoring of electrical parameters of different computer devices. To measure energy consumption in a computer system, it is proposed to use, instead of a general-purpose wattmeter, one designed ad-hoc and synchronously collects the energy consumption of its various nodes
-
General-purpose data stream processing on heterogeneous architectures with WindFlow J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-10-10 Gabriele Mencagli, Massimo Torquati, Dalvan Griebler, Alessandra Fais, Marco Danelutto
Many emerging applications analyze data streams by running graphs of communicating tasks called operators. To develop and deploy such applications, Stream Processing Systems (SPSs) like Apache Storm and Flink have been made available to researchers and practitioners. They exhibit imperative or declarative programming interfaces to develop operators running arbitrary algorithms working on structured
-
Scheduling independent tasks on multiple cloud-assisted edge servers with energy constraint J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-10-06 Keqin Li
In this paper, we study task scheduling with or without energy constraint in mobile edge computing with multiple cloud-assisted edge servers as combinatorial optimization problems within the framework of classical scheduling theory. The first problem is to schedule a list of independent tasks on a mobile device and several heterogeneous edge servers and cloud servers, such that the makespan is minimized
-
Accelerating block lifecycle on blockchain via hardware transactional memory J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-10-04 Yue Li, Han Liu, Jianbo Gao, Jiashuo Zhang, Zhi Guan, Zhong Chen
The processing of block lifecycles is essential to the efficiency of a blockchain, which consists of four steps: creation, execution, consensus, and validation. The permissionless blockchain systems typically had very limited transaction throughput because of the performance bottleneck of consensus protocols. With recent advances in consensus protocols, the execution and validation of transactions
-
Tiny Twins for detecting cyber-attacks at runtime using concise Rebeca time transition system J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-10-04 Fereidoun Moradi, Bahman Pourvatan, Sara Abbaspour Asadollah, Marjan Sirjani
This paper presents a method for detecting cyber-attacks in cyber-physical systems using a monitor. The method employs an abstract model called Tiny Twin, which is built at design time and is used at runtime to detect inconsistencies. Tiny Twin is a state transition system that represents the observable behavior of the system from the monitor point of view. We model the behavior of the system in the
-
Redactable consortium blockchain based on verifiable distributed chameleon hash functions J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-09-26 Xiangyu Wu, Xuehui Du, Qiantao Yang, Na Wang, Wenjuan Wang
With the evolving application demands, the inherent immutability of consortium blockchains hinders their widespread adoption. For example, expired data stored on the chain cannot be deleted, and erroneous data cannot be redacted, seriously limiting the flexibility of consortium blockchains. However, existing redactable blockchain solutions need to be improved in aspects of decentralization, efficiency
-
Interference-aware opportunistic job placement for shared distributed deep learning clusters J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-09-25 Hongliang Li, Hairui Zhao, Ting Sun, Xiang Li, Haixiao Xu, Keqin Li
Distributed deep learning frameworks facilitate large deep learning workloads. These frameworks support sharing one GPU device among multiple jobs to improve resource utilization. Modern deep learning training jobs consume a large amount of GPU memory. Despite that, sharing GPU memory among jobs is still possible because a training job has iterative steps that its memory usage fluctuates over time
-
Privacy-Preserving Offloading Scheme in Multi-access Mobile Edge Computing Based on MADRL J. Parallel Distrib. Comput. (IF 3.8) Pub Date : 2023-09-21 Guowen Wu, Xihang Chen, Zhengjun Gao, Hong Zhang, Shui Yu, Shigen Shen
With the development of industrialization and intelligence, the Industrial Internet of Things (IIoT) has gradually become the direction for traditional industries to transform into modern ones. In order to adapt to the emergence of a large number of edge access devices such as sensors, as well as the demand for high-consumption and low-latency computing tasks, Mobile Edge Computing (MEC) has been proposed